1.1. Contact organisation
Statistical Office of the Republic of Serbia (SORS)
1.2. Contact organisation unit
Unit for statistics of education, science and culture
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Milana Rakica 5
11050 Belgrade
Serbia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
20 March 2026
2.2. Metadata last posted
6 April 2026
2.3. Metadata last update
6 April 2026
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Tertiary education institution | Fully included, in compliance with Frascatti Manual 2015. |
|---|---|
| University and colleges: core of the sector | Yes. |
| University hospitals and clinics | Not included (depends on primarily R&D activity). |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
Not included. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Yes, in compliance with Frascatti Manual 2015. |
|---|---|
| External R&D personnel | Only in cases when contracted for R&D. |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Not included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes. |
|---|---|
| Payments to rest of the world by sector - availability | Not available. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | Domestic funding: Planned budgetary funds dedicated to R&D from the ministries; General funds of the university/faculty; Funds for R&D from other government funds, agencies and foundations; Funds for R&D from local authorities’ bodies; Funds for R&D from enterprises; Funds from tertiary education institutions; Funds for R&D from non-profit organizations; Own funds of the reporting unit; Other funds for R&D from own sources and Funds from abroad: Funds from enterprises (in the same group, other enterprises outside the group); Funds for R&D from foreign governments; Funds for R&D from the university and other tertiary education institutions; Funds for R&D from non-profit organizations; Funds for R&D from the European Commission; Funds for R&D from international organizations; Other foreign funds |
| Type of R&D | Basic, applied research and experimental development |
| Type of costs | Labour costs, other current costs, capital expenditures, investment costs - no deviations. |
| Defence R&D - method for obtaining data on R&D expenditure | Counting records of the receipts and realised investments in R&D |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | The calendar year |
|---|---|
| Function | All occupations are classified as: researchers, professional associates, technicians and other personnel (supoer) |
| Qualification | For researchers and professional associates questionnaire requests levels 6-8 (ISCED-F 2013), for the others all levels |
| Age | Less than 25; 25-34; 35-44; 45-54; 55-64; 65 and more |
| Citizenship | Data are collected from 2007 onwards: citizenship - by geographical position of the country |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | The calendar year |
|---|---|
| Function | All occupations are classified as: researchers, professional associates, technicians, management staff and other personnel (supoer) |
| Qualification | For researchers and professional associates questionnaire requests levels 6-8 (ISCED-F 2013), for the others all levels |
| Age | Less than 25; 25-34; 35-44; 45-54; 55-64; 65 and more |
| Citizenship | Data are collected from 2007 onwards: citizenship - by geographical position of the country |
3.4.2.3. FTE calculation
R&D coefficients are used for the calculation and estimation of FTE data to report in line with FM.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | For the data collection on R&D in the HES sector SORS has a census for all kinds of R&D data. It includes faculties and universities with constituent units such as faculties, academies, and scientific research institutes, regardless of sources of funding and legal status. This sector also includes research institutes and clinics under the direct control or administration of a higher education institution. | |
| Estimation of the target population size |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
The 2023 calendar year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | National Statistical Legislation |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes. |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Yes.
- Confidentiality commitments of survey staff: Oath of Office.
7.2. Confidentiality - data treatment
Individual data can be given only to the owner of those data. Only the employees of the Office authorized by decision of the Director of the Office shall have access to confidential data from administrative sources.
Also, if SORS transmits data with a confidentiality flag or an embargo date, these data are not disseminated until the confidentiality flag is lifted in a subsequent data transmission or the embargo expired.
8.1. Release calendar
The data are available on the last day of August in the results published on the website of the Institute, according to the official calendar of data publishing Statistical Office Website
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
External users can find information in the Bulletin and in the Statistical Release on SORS' website
R&D Bulletin Statistical Office
In line with the Community legal framework and the European Statistics Code of Practice Eurostat disseminates European statistics on Eurostat's website respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Regular Releases |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Links |
|---|---|---|
| General publication/article | Y | R&D Bulletin Statistical Office |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | With special approval from Director. |
|---|---|
| Access cost policy | Not available. |
| Micro-data anonymisation rules | Proscribed by the "Procedure for granting access to individual data without identifiers (anonymized microdata)", the internal SORS document. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures | |
| Data prepared for individual ad hoc requests | Y | Both | Micro-data can be granted only with special approval from Director. |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Abbreviated methodology in the national language is available on the web site of the SORS.
R&D Methodological Notes and the ESS Metadata Handler.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Yes. |
|---|---|
| Requests on further clarification, most problematic issues | Upon request. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
The HES R&D statistics methodology is completely in line with FM methodology. Minor improvements can be achieved looking up possible R&D performers not detected yet to increase the coverage.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | PUBLIC SECTOR – national level: 1.1 State and public institution:Government of the RS; Ministries; Agencies, Governmental Offices; 1.2 Bodies of local communities 1.3 Chamber of commerce 1.4 Other agencies of public sector (public institutes, public agencies, institutes with public right, communities of institutes, public research institutes, etc.) |
Detailed data on capacity and trends of Serbian R&D performance for R&D and innovation and education policy decisions and strategy planning, analysis of changes in Serbian R&D performance together with international comparisons, statistics, analysis and access to microdata. |
| 2 | Economic companies | |
| 3 | Education, Science and research : 3.1 Education institutions: Universities, Faculties; Higher professional institutions; 3.2 Institutes and other research organizations: Public research institutes; Other institutes and research organizations; Institutes and research centres inside the universities; Registered researchers; Private researchers |
|
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | Statistical Office conducts the Survey on user satisfaction generally, not for particular indicator. |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Up to now we didn't use user satisfaction surveys. The national data delivered to the internationally requested data on the Eurostat/OECD harmonised R&D data collection. Practically there are no deviations in the classification of major fields of science and technology or variable deviations because the methodology is completely in line with Frascati methodology. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Data represents complete and consistent presentation of Higher Education Sector statistics, in accordance with Eurostat requirements.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Final variables only. |
| Obligatory data on R&D expenditure | No missing data. |
| Optional data on R&D expenditure | No missing data. |
| Obligatory data on R&D personnel | No missing data. |
| Optional data on R&D personnel | No missing data. |
| Regional data on R&D expenditure and R&D personnel | No missing data. |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | ||||
| Socioeconomic objective | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| No. | |||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available. | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | : | : | : | : | : | : | : |
| Total R&D personnel in FTE | : | : | : | : | : | : | : |
| Researchers in FTE | : | : | : | : | : | : | : |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | Yes | ||||
| Total R&D personnel in FTE | Yes | ||||
| Researchers in FTE | Yes |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not available. |
| Government | Not available. |
| Higher education | Not available. |
| Private non-profit | Not available. |
| Rest of the world | Not available. |
| Total | Not available. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not available. |
| Technicians | Not available. | |
| Other support staff | Not available. | |
| Qualification | ISCED 8 | Not available. |
| ISCED 5-7 | Not available. | |
| ISCED 4 and below | Not available. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: Considered to be small.
- Measures taken to reduce their effect: No measures needed
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: Considered to be small.
- Measures taken to reduce their effect: No measures needed.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 110 | 110 | 0% |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | |
| Comments | No missing data. | No missing data. |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Not applicable |
|---|---|
| Estimates of data entry errors | Not applicable |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | Not applicable |
| Editing process and method | Not applicable |
| Procedure used to correct errors | Not applicable |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: T+10
- Lag (days): 300
14.1.2. Time lag - final result
- End of reference period: 31 December: 2023
- Date of first release of national data: T+18
- Lag (days): 545
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | no delay | no delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
The comparability is ensured by the application of common definitions and methodological framework based on internationally methodologies and standards.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviations. | |
| Researcher | FM2015, § 5.35-5.39. | No deviations. | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviations. | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviations. | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviations. | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No deviations. |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviations. | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviations. | |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviations. | |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviations. | |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No deviations. |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 2008 | ||
| Function | 2008 | Due to a changes in methodology and to program for data processing comparability over time is fully possible from 2008. | |
| Qualification | 2008 | ||
| R&D personnel (FTE) | 2008 | ||
| Function | 2008 | ||
| Qualification | 2008 | ||
| R&D expenditure | 2008 | ||
| Source of funds | 2008 | ||
| Type of costs | 2008 | ||
| Type of R&D | 2008 | ||
| Other |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Survey have been using same tools and rules and is a comprehensive in all calendar years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics are largely harmonized with the National Accounts. The SNA mainly uses R&D data to calculate preliminary GDP estimates and/or other calculations. More detailed information is not available at the moment.
15.3.3. Coherence – Education statistics
The Statistical Office is in charge for collecting the UOE data, most notably Department for Education, Science and Culture for all questionnaires except Educational expenditures which is filled by Department of National Accounts. Questionnaire on ISCED mapping is under the jurisdiction of Ministry of Education.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | |||
| Final data (delivered T+18) | |||
| Difference (of final data) |
Comments:
Final data have been transmitted.
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | ||
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available separately. | |
| Data collection costs | Not available separately. | |
| Other costs | Not available separately. | |
| Total costs |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 59 | |
| Average Time required to complete the questionnaire in hours (T)1) | Not available | |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
Data on research and development were collected by regular annual survey since 1965. Since 2007, the applied methodology has been harmonized with the international: Frascati Manual. The survey includes: all research and development organizations, regardless of whether it is their main activity or not. The basic source of data is the accounting records of costs and investments made in research and development. The annual report on research and experimental development provides data on resources (revenues, expenditures and investments) by type of research, sources of financing and purposes, and data for human capital as well. All data is available by type of research organization, according to fields of science, type of research activity, type of employment and length of working hours of employees, level of economic activities (KD2010 = NACE2) and according to the territorial principle (to the level of NUTS2). The basic set of survey is all organizations that are engaged in research and development (R&D), regardless of whether it is their core activity or not.
18.1.2. Sample/census survey information
| Sampling unit | Not a sample survey. |
|---|---|
| Stratification variables (if any - for sample surveys only) | census survey |
| Stratification variable classes | census survey |
| Population size | census survey |
| Planned sample size | census survey |
| Sample selection mechanism (for sample surveys only) | census survey |
| Survey frame | census survey |
| Sample design | census survey |
| Sample size | census survey |
| Survey frame quality | census survey |
| Variables the survey contributes to | census survey |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No use of administrative data or pre-compiled statistics. |
|---|---|
| Description of collected data / statistics | census survey |
| Reference period, in relation to the variables the administrative source contributes to | |
| Variables the administrative source contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | SORS |
|---|---|
| Description of collected information | Data are collected using the postal survey, questionnaries sent by email or web questionnaire. |
| Data collection method | Census |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | The R&D survey is obligatory according to the Serbian national statistics act and national statistical programme. The questionnaire is sent to reporting units by post in the beginning od April. Enterprises that did not return answered questionnaires in time are reminded 2 times by remind-leters. Some important missing reporting units are reminded also by telephone. |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | R&D National Survey (English) |
| R&D national questionnaire and explanatory notes in the national language: | R&D National Survey (Serbian) |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: | R&D Methodological Note (Serbian) |
18.4. Data validation
Data for science are checked for accuracy and completeness. Transmitted figures are screened both internally, by SORS and externally, by Eurostat.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | census survey |
|---|---|
| Data compilation method - Preliminary data |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Not applicable. |
| Revision policy for the coefficients | Not applicable. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable. |
18.5.4. Measurement issues
| Method of derivation of regional data | Micro data are processed to the level of NUTS 3, but published only on the level of NUTS 2 |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” |
18.5.5. Weighting and estimation methods
| Description of weighting method | |
|---|---|
| Description of the estimation method |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Due to national characteristics and specifities, mainly in method and data compilation, some concepts are not applicable in R&D survey in Republic of Serbia.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
6 April 2026
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993
See below.
Not requested. R&D statistics cover national and regional data.
The 2023 calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
